Modeling method and device of power battery and vehicle

Abstract

The invention relates to the technical field of power batteries, in particular to the technical field of battery modeling, in particular to a modeling method and device of a power battery and a vehicle, and aims at conducting more accurate battery modeling. The method comprises the steps that a battery single body state model of a first battery single body is constructed; wherein the battery cell state model is used for determining a state parameter value of the first battery cell based on an observation parameter actual value of the first battery cell; determining a state parameter value of the second battery monomer by using the difference between the actual values of the observation parameters between the first battery monomer and the second battery monomer and the state parameter value of the first battery monomer; the second battery monomer is the battery monomer except the first battery monomer in the battery monomers; the battery state model with coupled electricity, heat and health states of the power battery is constructed by using the observation parameter values and the state parameter values of the battery monomers, so that the model construction performance of the power battery is improved.

Description

Technical Field

[0001] This application relates to the field of power battery technology, and more particularly to the field of battery modeling technology, specifically to a power battery modeling method, device, and vehicle. Background Technology

[0002] Currently, the new energy vehicle industry is booming. As a core component of vehicles, the performance of power batteries directly affects the safety, reliability, and range of vehicles. Therefore, multiple performance tests are required before and after the power batteries are deployed in vehicles. Performance testing generally involves modeling the power battery and conducting multiple simulation tests.

[0003] However, in existing technologies, when modeling individual cells of power batteries, the battery pack is generally treated as a uniform whole, meaning that each battery cell is considered to have the same performance. In actual applications of power batteries, the state parameters of each battery cell are different. Therefore, there is an urgent need for a more accurate modeling method for power batteries to improve the performance of vehicle-in-the-loop testing. Summary of the Invention

[0004] This application provides a modeling method, apparatus, and vehicle for power batteries, to at least address the technical problem in related technologies where there is a significant error between the model prediction results of battery state models and the actual state of the power battery. The technical solution adopted in this application is as follows: In a first aspect, this application provides a modeling method for a power battery, comprising: constructing a battery cell state model of a first battery cell; wherein the battery cell state model is used to determine the state parameter values ​​of the first battery cell based on the actual values ​​of the observed parameters of the first battery cell; the first battery cell is any battery cell among the battery cells included in the power battery; using the difference in the actual values ​​of the observed parameters between the first battery cell and the second battery cell and the state parameter values ​​of the first battery cell, the state parameter values ​​of the second battery cell are determined; the second battery cell is a battery cell other than the first battery cell; and using the observed parameter values ​​and state parameter values ​​of the battery cells, constructing a battery state model that couples the electrical, thermal, and health states of the power battery.

[0005] Based on the aforementioned technical means, this application models the first battery cell in the power battery and determines the state parameter value of the second battery cell based on the state parameter value of the first battery cell and the cell difference between the first and second battery cells. This reduces the amount of modeling required for the battery cells, improves the modeling efficiency of the battery cells, and utilizes the observed parameter values ​​and state parameter values ​​of the battery cells to construct a battery state model of the power battery. This allows for a more comprehensive consideration of the electrical, thermal, and health conditions of the power battery, enabling the constructed battery state model to more accurately reflect the actual state of the power battery and improve the modeling performance of the battery state model.

[0006] In one possible implementation, the state parameters referred to by the state parameter values ​​include: electrical state parameters, thermal state parameters, and health state parameters. A battery state model coupling the electrical, thermal, and health states of the power battery is constructed using the observed parameter values ​​and state parameter values ​​of individual battery cells. This includes: constructing a battery cell state transition function; where the battery cell state transition function characterizes the relationship between the state parameter values ​​of the battery cell at the first target time and the predicted state parameter values ​​of the battery cell at the second target time, given the observed parameter values ​​of the battery cell at the first target time; where the second target time is the time following the first target time; and the relationship between the state parameters is a coupled relationship between electrical parameters, thermal parameters, and health state parameters; constructing a battery cell observation function; where the battery cell observation function characterizes the correspondence between the predicted state parameter values ​​and the observed parameter prediction values ​​of the battery cell at the second target time; constructing a correction function; where the correction function corrects the predicted state parameter values ​​of the battery cell at the second target time given the actual observed parameter values ​​of the battery cell at the second target time; and constructing a battery state model based on the battery cell state transition function, battery cell observation function, correction function, and the observed and state parameter values ​​of the battery cells.

[0007] Based on the aforementioned technical means, this application achieves prediction of multi-dimensional parameters coupled with electrical, thermal, and health parameters through a state transition function, establishes the correlation between state parameters and observed parameters through an observation function, and optimizes the predicted values ​​of state parameters in real time through a correction function. Thus, based on the battery cell state transition function, battery cell observation function, correction function, and the observed and state parameter values ​​of the battery cell, a battery state model coupled with electrical, thermal, and health states is constructed. This model can more accurately depict the battery state of the power battery under multi-dimensional electrical, thermal, and health states, thereby improving the comprehensiveness and accuracy of battery state estimation.

[0008] In one possible implementation, the process of constructing the correction function includes: constructing a first mapping relationship; wherein the first mapping relationship is used to characterize the correspondence between the observation residual of a battery cell at the second target time and the actual value and predicted value of the observation parameter of the battery cell at the second target time; constructing a second mapping relationship; wherein the second mapping relationship is used to characterize the correspondence between the gain coefficient of each state parameter of the battery cell at the second target time and the observation residual; constructing a third mapping relationship; wherein the third mapping relationship is used to characterize the correspondence between the state parameter correction value, gain coefficient and predicted value of the state parameter of each state parameter of the battery cell at the second target time; and generating a correction function based on the first mapping relationship, the second mapping relationship and the third mapping relationship.

[0009] Based on the aforementioned technical means, this application generates a correction function by combining the first mapping relationship, the second mapping relationship, and the third mapping relationship, which can use the actual values ​​of the observed parameters to correct the predicted values ​​of the observed parameters in real time and accurately.

[0010] In one possible implementation, the electrical state parameters include the internal resistance of the battery cell; the thermal state parameters include the temperature of the battery cell; the health state parameters include the state of charge of the battery cell; and the observed parameters indicated by the actual values ​​include the voltage of the battery cell.

[0011] Based on the aforementioned technical means, this application predicts the electrical state parameters, thermal state parameters, and health state parameters of a single battery cell, and then corrects the state parameter values ​​based on the prediction results, thereby enabling a more comprehensive and accurate prediction of the state of a single battery cell.

[0012] In one possible implementation, the battery cell state transition function includes: a battery cell state of charge transition function, a battery cell temperature transition function, and a battery cell internal resistance transition function; wherein, the battery cell state of charge transition function characterizes the state of charge of the battery cell at a second target time, and the correspondence between the battery cell state of charge at the first target time and the battery cell current; the battery cell temperature transition function characterizes the battery cell temperature at the second target time, and the correspondence between the battery cell temperature at the first target time, the battery cell current, the battery cell internal resistance, and a first temperature difference; the first temperature difference is the difference between the battery cell temperature and the ambient temperature; the battery cell internal resistance transition function characterizes the battery cell internal resistance at the second target time, and the correspondence between the battery cell internal resistance, the reference internal resistance, the thermal conductivity coefficient, and the second temperature difference; the second temperature difference is the temperature difference between the battery cell temperature at the second target time and the battery cell temperature at the second target time of the first battery cell.

[0013] Based on the above technical means, this application can more accurately determine the charge change of a battery cell through the battery cell state of charge transfer function, more accurately grasp the temperature change of a battery cell through the temperature transfer function, and more timely and accurately reflect the aging and performance degradation of the battery through the internal resistance transfer function. The results of the above three battery cell state of charge transfer functions can more comprehensively and meticulously depict the state of the battery cell.

[0014] In one possible implementation, the battery cell observation function characterizes the relationship between the battery cell voltage at the second target time and the battery cell open-circuit voltage, convective heat transfer coefficient, and second temperature difference of the first battery cell at the second target time; wherein, the second temperature difference is the temperature difference between the battery cell temperature at the second target time and the battery cell temperature of the first battery cell at the second target time.

[0015] Based on the above technical means, this application can more clearly determine the relationship between the battery cell voltage and open circuit voltage, the battery cell internal resistance and the cell current by setting the battery cell observation function, thereby predicting the battery cell voltage more quickly and accurately.

[0016] In one possible implementation, the second mapping relationship is used to characterize the correspondence between the gain coefficient of each state parameter of a battery cell at the second target time and the observation noise covariance, prior error covariance, and Jacobian matrix of each state parameter of the battery cell at the second target time. The prior error covariance is determined based on the Jacobian matrix and the process noise covariance and posterior error covariance of each state parameter of the battery cell at the second target time. The process noise covariance and observation noise covariance are determined based on the observation residuals. The Jacobian matrix is ​​used to characterize the partial derivative of the predicted observation parameter value of the battery cell at the second target time with respect to the predicted state parameter value of each state parameter of the battery cell at the second target time. The posterior error covariance is determined based on the prior error covariance and the gain coefficient.

[0017] Based on the above technical means, this application determines the noise covariance matrix based on the observation residual, determines the prior error covariance based on the process noise covariance, and then determines the Kalman gain based on the observation noise covariance, the prior error covariance, and the Jacobian matrix. This can reduce the deviation between the predicted and actual values ​​of the state parameters, making the state parameter values ​​adjusted based on the gain coefficient closer to the actual values ​​of the state parameters, thereby improving the accuracy and robustness of the battery cell state prediction and reducing measurement noise and estimation errors.

[0018] In one possible implementation, the third mapping relationship is used to characterize the correspondence between the state parameter correction value of each state parameter of the battery cell at the second target time, the state parameter correction increment, and the state parameter prediction value; wherein, the state parameter correction increment is determined based on the polarization voltage of the battery cell at the second target time and the gain coefficient of each state parameter of the battery cell at the second target time.

[0019] Based on the above technical means, this application determines the corresponding parameter correction increment by the gain coefficient corresponding to each state parameter, thereby correcting the state parameter value of each state parameter, so that the corrected state parameter value can be closer to the actual state parameter value of the battery cell, thereby more accurately determining the battery state model.

[0020] In one possible implementation, a battery state model is constructed based on the battery cell state transition function, battery cell observation function, correction function, and the observed and state parameter values ​​of the battery cells. This includes: constructing a verification model; wherein the verification model is used to verify whether the reconstructed cell voltage at the second target time satisfies preset physical constraints; and constructing a battery state model using the battery cell state transition function, battery cell observation function, correction function, verification model, and the observed and state parameter values ​​of the battery cells; wherein the reconstructed cell voltage satisfying the preset physical constraints is used to replace the actual observed parameter values ​​of the battery cells at the second target time in the battery state model.

[0021] Based on the above technical means, this application uses a verification model to verify individual battery cells and replaces the actual values ​​of observed parameters with the reconstructed cell voltage that meets the physical constraints, so as to construct a battery state model of the power battery with the actual values ​​of the state parameters, which enables the battery state model to better fit the actual working conditions of the power battery.

[0022] In one possible implementation, the battery cell state model includes: a voltage model, a thermal effect model, and a health state model; wherein, the voltage model is used to characterize the correspondence between the battery cell voltage and the battery cell electromotive force and the battery cell voltage loss; the voltage loss includes polarization loss and inductive reactance voltage loss; the thermal effect model is used to characterize the correspondence between the battery cell temperature and the battery cell initial temperature, specific heat capacity, battery cell thermal power, and battery cell mass; the health state model is used to characterize the correspondence between the battery cell state of charge and the battery cell initial state of charge, the battery cell rated capacitance, the battery cell actual capacitance, and the battery cell net current; the battery cell net current is the difference between the battery cell open-circuit current and the battery cell loss current.

[0023] Based on the above technical means, this application uses a battery cell state model based on voltage model, thermal effect model and health state model to predict the state parameters of battery cells, which can make the prediction of battery cells more comprehensive and more accurately reflect the actual state of battery cells.

[0024] Secondly, this application provides a modeling apparatus for a power battery, comprising: a single-cell modeling module for constructing a single-cell state model of a first battery cell; wherein the single-cell state model is used to determine the state parameter values ​​of the first battery cell based on the actual values ​​of the observed parameters of the first battery cell; the first battery cell is any battery cell included in the power battery; a parameter determination module for determining the state parameter values ​​of the second battery cell by utilizing the difference in the actual values ​​of the observed parameters between the first battery cell and the second battery cell; the second battery cell is a battery cell other than the first battery cell; and a model construction module for constructing a battery state model of the power battery that couples the electrical, thermal, and health states using the observed parameter values ​​and state parameter values ​​of the battery cells.

[0025] In one possible implementation, the state parameters referred to by the state parameter values ​​include: electrical state parameters, thermal state parameters, and health state parameters; a model building module is used to construct the battery cell state transition function; wherein, the battery cell state transition function is used to characterize the state parameter change relationship between the state parameter value of the battery cell at the first target time and the predicted state parameter value of the battery cell at the second target time, given the observed parameter value of the battery cell at the first target time; wherein, the second target time is the time following the first target time; the state parameter change relationship is a coupled change relationship of electrical parameters, thermal parameters, and health state parameters; a battery cell observation function is constructed; wherein, the battery cell observation function characterizes the correspondence between the predicted state parameter value and the observed parameter prediction value of the battery cell at the second target time; a correction function is constructed; wherein, given the actual observed parameter value of the battery cell at the second target time, the correction function is used to correct the predicted state parameter value of the battery cell at the second target time; based on the battery cell state transition function, the battery cell observation function, the correction function, and the observed parameter value and state parameter value of the battery cell, a battery state model is constructed.

[0026] In one possible implementation, the process of constructing the correction function includes: constructing a first mapping relationship; wherein the first mapping relationship is used to characterize the correspondence between the observation residual of a battery cell at the second target time and the actual value and predicted value of the observation parameter of the battery cell at the second target time; constructing a second mapping relationship; wherein the second mapping relationship is used to characterize the correspondence between the gain coefficient of each state parameter of the battery cell at the second target time and the observation residual; constructing a third mapping relationship; wherein the third mapping relationship is used to characterize the correspondence between the state parameter correction value, gain coefficient and predicted value of the state parameter of each state parameter of the battery cell at the second target time; and generating a correction function based on the first mapping relationship, the second mapping relationship and the third mapping relationship.

[0027] In one possible implementation, the electrical state parameters include the internal resistance of the battery cell; the thermal state parameters include the temperature of the battery cell; the health state parameters include the state of charge of the battery cell; and the observed parameters indicated by the actual values ​​include the voltage of the battery cell.

[0028] In one possible implementation, the battery cell state transition function includes: a battery cell state of charge transition function, a battery cell temperature transition function, and a battery cell internal resistance transition function; wherein, the battery cell state of charge transition function characterizes the state of charge of the battery cell at a second target time, and the correspondence between the battery cell state of charge at the first target time and the battery cell current; the battery cell temperature transition function characterizes the battery cell temperature at the second target time, and the correspondence between the battery cell temperature at the first target time, the battery cell current, the battery cell internal resistance, and a first temperature difference; the first temperature difference is the difference between the battery cell temperature and the ambient temperature; the battery cell internal resistance transition function characterizes the battery cell internal resistance at the second target time, and the correspondence between the battery cell internal resistance, the reference internal resistance, the thermal conductivity coefficient, and the second temperature difference; the second temperature difference is the temperature difference between the battery cell temperature at the second target time and the battery cell temperature at the second target time of the first battery cell.

[0029] In one possible implementation, the battery cell observation function is used to characterize the relationship between the battery cell voltage at the second target time and the battery cell open-circuit voltage, convective heat transfer coefficient, and second temperature difference of the first battery cell at the second target time; wherein, the second temperature difference is the temperature difference between the battery cell temperature at the second target time and the battery cell temperature of the first battery cell at the second target time.

[0030] In one possible implementation, the second mapping relationship is used to characterize the correspondence between the gain coefficient of each state parameter of a battery cell at the second target time and the observation noise covariance, prior error covariance, and Jacobian matrix of each state parameter of the battery cell at the second target time. The prior error covariance is determined based on the Jacobian matrix and the process noise covariance and posterior error covariance of each state parameter of the battery cell at the second target time. The process noise covariance and observation noise covariance are determined based on the observation residuals. The Jacobian matrix is ​​used to characterize the partial derivative of the predicted observation parameter value of the battery cell at the second target time with respect to the predicted state parameter value of each state parameter of the battery cell at the second target time. The posterior error covariance is determined based on the prior error covariance and the gain coefficient.

[0031] In one possible implementation, the third mapping relationship is used to characterize the correspondence between the state parameter correction value of each state parameter of the battery cell at the second target time, the state parameter correction increment, and the state parameter prediction value; wherein, the state parameter correction increment is determined based on the polarization voltage of the battery cell at the second target time and the gain coefficient of each state parameter of the battery cell at the second target time.

[0032] In one possible implementation, the model building module is also used to build a verification model; wherein the verification model is used to verify whether the reconstructed cell voltage of the battery cell at the second target time meets the preset physical constraints; the battery state model is constructed using the battery cell state transition function, battery cell observation function, correction function, verification model, and the observed parameter values ​​and state parameter values ​​of the battery cell; wherein the battery state model uses the reconstructed cell voltage that meets the preset physical constraints to replace the actual values ​​of the observed parameters of the battery cell at the second target time.

[0033] In one possible implementation, the battery cell state model includes: a voltage model, a thermal effect model, and a health state model; wherein, the voltage model is used to characterize the correspondence between the battery cell voltage and the battery cell electromotive force and the battery cell voltage loss; the voltage loss includes polarization loss and inductive reactance voltage loss; the thermal effect model is used to characterize the correspondence between the battery cell temperature and the battery cell initial temperature, specific heat capacity, battery cell thermal power, and battery cell mass; the health state model is used to characterize the correspondence between the battery cell state of charge and the battery cell initial state of charge, the battery cell rated capacitance, the battery cell actual capacitance, and the battery cell net current; the battery cell net current is the difference between the battery cell open-circuit current and the battery cell loss current.

[0034] Thirdly, this application provides a vehicle including a power battery; the power battery is tested in-loop using a battery state model; the battery state model is constructed using the power battery modeling device described in the second aspect.

[0035] Fourthly, this application provides an electronic device, including: a processor and a memory, wherein the memory stores at least one computer program, and the at least one computer program is loaded and executed by the processor to implement the method described in the first aspect and any possible implementation thereof.

[0036] Fifthly, this application provides a computer-readable storage medium that, when the instructions in the computer-readable storage medium are executed by a processor of an electronic device, enables the electronic device to perform the methods described in the first aspect and any possible implementation thereof.

[0037] In a sixth aspect, this application provides a computer program product comprising computer instructions that, when executed on an electronic device, cause the electronic device to perform the method described in the first aspect and any of its possible implementations.

[0038] It should be noted that the technical effects of any of the implementation methods in aspects two through six can be found in the technical effects of the corresponding implementation methods in aspect one, and will not be repeated here.

[0039] It should be understood that the above general description and the following detailed description are exemplary and explanatory only, and do not limit this application. Attached Figure Description

[0040] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application, and do not constitute an undue limitation of this application.

[0041] Figure 1 This is a schematic diagram of the structure of a vehicle shown in an embodiment of this application; Figure 2 This is a schematic diagram of the structure of a power battery shown in an embodiment of this application; Figure 3 This is a schematic diagram of the structure of a vehicle in-loop test model shown in an embodiment of this application; Figure 4 This is a flowchart illustrating a modeling method for a power battery according to an embodiment of this application; Figure 5 This is a schematic diagram of the state model of the power battery shown in the embodiments of this application; Figure 6 This is a block diagram of a modeling device for a power battery, as shown in an embodiment of this application. Figure 7 This is a block diagram illustrating an electronic device according to an embodiment of this application. Detailed Implementation

[0042] To enable those skilled in the art to better understand the technical solutions of this application, the technical solutions in the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings.

[0043] It should be noted that the terms "first," "second," etc., used in the specification, claims, and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of this application described herein can be implemented in orders other than those illustrated or described herein. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with this application. Rather, they are merely examples of apparatuses and methods consistent with some aspects of this application as detailed in the appended claims.

[0044] In the embodiments of this application, the words "exemplary," "for example," or "for instance" are used to indicate examples, illustrations, or explanations. Any embodiment or design described as "exemplary," "for example," or "for instance" in the embodiments of this application should not be construed as being more preferred or advantageous than other embodiments or designs. Specifically, the use of the words "exemplary," "for example," or "for instance" is intended to present the relevant concepts in a specific manner.

[0045] The technical solutions of the embodiments of this application will be described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments.

[0046] The power battery modeling apparatus provided in this application is used to model the power battery of a vehicle (especially an intelligent driving vehicle) so as to perform in-loop testing of the vehicle based on the established power battery state model. A vehicle can also be referred to as a vehicle, mobile carrier, electric vehicle (EV), hybrid electric vehicle (HEV), plug-in hybrid electric vehicle (PHEV), fuel cell vehicle (FCV), autonomous vehicle, intelligent and connected vehicle (ICV), driverless vehicle, etc.

[0047] In this application, the vehicle can be a sedan, a sport utility vehicle (SUV), a truck, a special vehicle (such as an ambulance, fire truck, police car, etc.), a driverless taxi, a smart connected bus, an autonomous logistics vehicle, an electric truck, etc. Furthermore, this method is also applicable to various special-purpose vehicles, such as agricultural vehicles, mining vehicles, forestry vehicles, airport vehicles, and port vehicles. This application does not impose specific limitations in this regard.

[0048] Figure 1 This is a schematic diagram of the structure of a vehicle shown in an embodiment of this application.

[0049] In one possible implementation, such as Figure 1 As shown, the vehicle 100 includes a power battery modeling device 101, a data acquisition device 102, a vehicle control device 103, and a power battery 104.

[0050] The data acquisition device 102 is used to monitor parameters such as voltage, current, temperature, state of charge, and health status of individual cells in the power battery 104 in real time, and transmits the parameters to the power battery modeling device 101.

[0051] The power battery modeling device 101 is used to predict the state of the power battery based on the parameters of the battery cells and the battery state model transmitted by the data acquisition device 102.

[0052] The vehicle control unit 103 is used to perform in-loop testing of the vehicle based on the parameter values ​​of the state parameters of the power battery predicted in the power battery modeling unit 101.

[0053] In practical applications, the power battery modeling device 101 can communicate with one or more data acquisition devices 102, and the power battery modeling device 101 can communicate with one or more vehicle control devices 103.

[0054] For ease of understanding, this application uses the establishment of a communication connection between a data acquisition device 102 and a power battery modeling device 101 as an example for illustration.

[0055] As a feasible approach, Figure 1 The power battery modeling device 101 can be installed inside or outside the vehicle. The data acquisition device 102 and the vehicle control device 103 are installed inside the vehicle. The power battery modeling device 101 and the data acquisition device 102 can be functional modules integrated into the same device or independent devices. This application does not impose any limitations on comparison.

[0056] It is easy to understand that when the power battery modeling device 101 and the data acquisition device 102 are functional modules integrated within the same device, the communication method between the power battery modeling device 101 and the data acquisition device 102 is the same as the communication between modules within the device. In this case, the communication process between the power battery modeling device 101, the data acquisition device 102, and the vehicle control device 103 is the same as the communication process when the power battery modeling device 101 and the data acquisition device 102 are set up independently. For ease of understanding, this application mainly uses the example of the power battery modeling device 101, the data acquisition device 102, and the vehicle control device 103 being set up independently for explanation.

[0057] As a feasible approach, Figure 1 The power battery modeling device 101 can be set on a terminal, a server, or other types of electronic devices.

[0058] When the power battery modeling device 101 is located at a terminal, the terminal can be a device providing data connectivity to vehicle users or vehicle owners, a handheld device with wireless connectivity, or other processing devices connected to a wireless modem. The terminal can communicate with one or more core networks via a radio access network (RAN). The terminal can be a mobile terminal, such as a computer with a mobile terminal, or a mobile device that exchanges voice and / or data with the radio access network, such as a mobile phone, tablet, laptop, netbook, or personal digital assistant (PDA). This application does not impose any limitations on this.

[0059] When the power battery modeling device 101 is located on a server, the server can be a single server or a server cluster consisting of multiple servers. In some embodiments, the server cluster can also be a distributed cluster. This application does not impose any limitations on this.

[0060] It should be noted that the structure illustrated in the embodiments of this application does not constitute a limitation on the modeling device 101 for the power battery. It may include more or fewer components than illustrated, or combine some components, or split some components, or have different component arrangements. The illustrated components may be implemented in hardware, software, or a combination of both.

[0061] Figure 2 This is a schematic diagram of the structure of a power battery shown in an embodiment of this application, with reference to... Figure 2 The power battery 200 includes a battery module 201 and a battery management system 202, wherein the battery module includes multiple battery cells.

[0062] The aforementioned power battery 200 is a storage battery used to provide power for electric vehicles or other power equipment. Electric vehicles include battery-powered vehicles, electric bicycles, electric trains, etc. Common types of power batteries include lithium-ion power batteries and nickel-metal hydride power batteries.

[0063] The aforementioned battery module 201 is a modular unit composed of multiple battery cells connected in series or in parallel. The battery module 201 typically includes battery cells, connectors, and a housing. The main function of the battery module is to connect multiple battery cells to increase the voltage and energy storage capacity of the power battery in order to meet the power needs of different devices and applications.

[0064] The aforementioned battery cell is the smallest electrochemical unit that makes up a battery module and is also the most basic component of a power battery. A battery cell is generally an electrochemical device encapsulated in a casing, consisting of a positive electrode, a negative electrode, a separator, and an electrolyte.

[0065] The aforementioned battery management system 202 is a device used to monitor the status of energy storage batteries, prevent overcharging or over-discharging of power batteries, and extend the service life of batteries.

[0066] Figure 3 This is a schematic diagram of the structure of a vehicle in-loop test model according to an embodiment of this application; the vehicle in-loop test model includes a VCU model 301, a BMS model 302 and a power model 303, wherein the VCU model 301 is used to receive driving parameter input and the power model 303 is used to output parameters.

[0067] The aforementioned driving parameter inputs are used to provide signals for interaction between the driver and the VCU, including CAN signals and hardwired signals, such as accelerator pedal opening, brake pedal opening, gear position signals, etc.

[0068] The above VCU model includes a VCU signal model and a real VCU device under test. This model is the main body of the test object.

[0069] The BMS model described above is used to simulate the battery's SOC state, charging and discharging functions, provide high voltage to the motor in the power model, and interact with the VCU and the power model.

[0070] The aforementioned dynamic model includes a motor, tires, etc., and provides dynamic parameters such as torque and speed.

[0071] The above parameter outputs are used to display the measured parameters in the above model on the host computer.

[0072] For ease of understanding, the modeling method for the power battery provided in this application will be described in detail below with reference to the accompanying drawings.

[0073] Figure 4 This is a flowchart illustrating a modeling method for a power battery according to an embodiment of this application, with reference to... Figure 4 The method includes: S401. Construct a battery cell state model for the first battery cell; wherein, the battery cell state model is used to determine the state parameter values ​​of the first battery cell based on the actual values ​​of the observed parameters of the first battery cell; the first battery cell is any battery cell among the battery cells included in the power battery.

[0074] The first battery cell mentioned above is a battery cell selected from the battery cells of the power battery to establish the battery cell state model. The first battery cell can be one battery cell or multiple battery cells.

[0075] The aforementioned battery cell state model is a mathematical model used to describe the internal state changes and external characteristics of a battery cell during charging and discharging. The battery cell state model includes a voltage model, a thermal effect model, and a health state model.

[0076] The voltage model described above is a mathematical model used to describe the voltage distribution and changes in a circuit.

[0077] The above thermal effect model is based on the laws of thermodynamics and the principles of chemical reaction kinetics and is used to analyze the thermal effect behavior of battery cells under different conditions.

[0078] The aforementioned health status model is established based on the battery's electrochemical characteristics, charge and discharge history, and current battery current, temperature, and other parameters. It is used to estimate the battery's state of charge. The more accurate the health status model, the more precise the judgment ability of the battery management system.

[0079] The aforementioned observation parameters refer to the electrical parameters of the battery cells that can be detected in the battery cells of the power battery. Among them, the actual values ​​of the observation parameters indicate the battery cell voltage.

[0080] The aforementioned state parameter values ​​refer to the state parameter values ​​of a battery cell predicted and calculated based on the battery cell state model.

[0081] The state parameters mentioned above refer to the following: electrical state parameters, thermal state parameters, and health state parameters; electrical state parameters include the internal resistance of individual battery cells; thermal state parameters include the temperature of individual battery cells; and health state parameters include the state of charge of individual battery cells.

[0082] S402. Using the difference in actual observed parameters between the first and second battery cells and the state parameter values ​​of the first battery cell, determine the state parameter values ​​of the second battery cell; the second battery cell is a battery cell other than the first battery cell.

[0083] The aforementioned second battery cell refers to the battery cell other than the first battery cell in the power battery. The state model of the second battery cell is not established. The state parameter value of the second battery cell is determined only based on the difference between the actual values ​​of the observed parameters in the second battery cell and the first battery cell.

[0084] The difference in actual values ​​of the aforementioned observation parameters refers to the difference between the actual voltage values ​​of the first and second battery cells, which are preset in advance. The difference in actual values ​​of the observation parameters can be determined based on the historical voltage values ​​of the battery cells of the power battery.

[0085] S403. Using the observed parameter values ​​and state parameter values ​​of individual battery cells, construct a battery state model that couples the electrical, thermal, and health states of the power battery.

[0086] The aforementioned coupling of electrical, thermal, and health states refers to the process of jointly modeling voltage models, thermal effect models, and health state models through their interactions and influences, in order to more comprehensively describe and predict the performance, temperature changes, and health state of individual battery cells.

[0087] The aforementioned battery state model is constructed based on physical principles and is a physical model used to describe the overall performance, state changes, and behavior prediction of a power battery. The input of the battery state model is the observed parameter value and state parameter value of each battery cell, and the output is the observed parameter value and state parameter value of the power battery.

[0088] Based on the aforementioned technical means, this application models the first battery cell in the power battery and determines the state parameter value of the second battery cell based on the state parameter value of the first battery cell and the cell difference between the first and second battery cells. This reduces the amount of modeling required for the battery cells, improves the modeling efficiency of the battery cells, and utilizes the observed parameter values ​​and state parameter values ​​of the battery cells to construct a battery state model of the power battery. This allows for a more comprehensive consideration of the electrical, thermal, and health conditions of the power battery, enabling the constructed battery state model to more accurately reflect the actual state of the power battery and improve the modeling performance of the battery state model.

[0089] As a feasible approach, Figure 5 This is a schematic diagram of the state model of the power battery shown in the embodiments of this application, with reference to... Figure 5 The state model of the power battery includes the voltage model 501, thermal effect model 502, and health state model 503 of the battery cell, as well as the battery state model 504 of the power battery.

[0090] In one possible implementation, a battery state model coupling the electrical, thermal, and health states of the power battery is constructed using the observed and state parameter values ​​of individual battery cells, including: Construct a state transition function for a single battery cell; wherein, the state transition function is used to characterize the relationship between the state parameter values ​​of the battery cell at the first target time and the predicted state parameter values ​​of the battery cell at the second target time, given the observed parameter values ​​of the battery cell at the first target time; wherein, the second target time is the time following the first target time; the relationship between the state parameters is a coupled relationship between electrical parameters, thermal parameters and health state parameters.

[0091] Construct a battery cell observation function; whereby the battery cell observation function characterizes the correspondence between the predicted values ​​of the state parameters and the predicted values ​​of the observation parameters of the battery cell at the second target time.

[0092] A correction function is constructed; this correction function is used to correct the predicted state parameters of a battery cell at the second target time, given the actual values ​​of the observed parameters of the battery cell at the second target time. Based on the battery cell state transition function, the battery cell observation function, the correction function, and the observed and state parameter values ​​of the battery cell, a battery state model is constructed, which can more accurately characterize the battery state of the power battery cell under multiple dimensions of electrical, thermal, and health conditions, thereby improving the comprehensiveness and accuracy of battery state estimation.

[0093] The first target time and the second target time mentioned above can be any time within the monitoring period and are relative concepts. The first target time is the time preceding the second target time.

[0094] The aforementioned predicted state parameters refer to the predicted state parameters of a battery cell at the second target time, determined based on the actual state parameters at the first target time and the battery cell state transition function.

[0095] The above-mentioned predicted values ​​of observation parameters refer to the predicted values ​​of the observed parameters during the process of modeling a battery cell. Among them, the predicted values ​​of observation parameters include the predicted values ​​of the battery cell voltage.

[0096] The above correction refers to the process of correcting the predicted values ​​of the state parameters at the corresponding time based on the correction function and the actual values ​​of the observed parameters.

[0097] The aforementioned battery cell state transition function is used to describe how the internal state of a battery cell changes with time or external input. For example, within the Kalman filter framework, the battery cell state transition function can be expressed as a discrete-time state equation:

[0098] In one possible implementation, the battery cell state transition function includes: the battery cell charge state transition function, the battery cell temperature transition function, and the battery cell internal resistance transition function.

[0099] Among them, the battery cell state-of-charge transition function is used to characterize the state of charge of the battery cell at the second target time, and the correspondence between the state of charge of the battery cell at the first target time and the battery cell current.

[0100] The battery cell temperature transfer function is used to characterize the battery cell temperature at the second target time, and the correspondence between the battery cell temperature at the first target time, the battery cell current, the battery cell internal resistance, and the first temperature difference; the first temperature difference is the difference between the battery cell temperature and the ambient temperature.

[0101] The battery cell internal resistance transfer function is used to characterize the battery cell internal resistance at the second target time and its correspondence with the reference internal resistance, thermal conductivity coefficient and the second temperature difference; the second temperature difference is the temperature difference between the battery cell temperature at the second target time and the battery cell temperature at the first target time.

[0102] As one feasible approach, the charge state transition function of a single battery cell is as follows:

[0103] in, Let be the prior estimate of the predicted state of charge (SOC) of the i-th battery cell at time k+1. Let be the posterior estimate of the predicted state of charge (SOC) of the i-th battery cell at time k. Let be the actual current value of the i-th battery cell at time k. This represents the time interval between adjacent moments.

[0104] The temperature transfer function of the above-mentioned battery cell is as follows:

[0105] in, Let be the prior estimate of the predicted temperature of the i-th battery cell at time k+1. Let be the posterior estimate of the predicted temperature of the i-th battery cell at time k. Let be the actual current value of the i-th battery cell at time k. The time interval between adjacent moments. Let be the specific heat capacity of the i-th battery cell. Let be the internal resistance of the i-th battery cell at time k. This refers to the ambient temperature where the power battery is located.

[0106] The internal resistance transfer function of the above-mentioned battery cell is as follows:

[0107] in, For the i-th battery cell at a temperature of In this case, the prior estimate of the predicted value of the internal resistance of a single battery cell is... Let be the reference internal resistance of the i-th battery cell. Let be the thermal conductivity coefficient of the i-th battery cell. The temperature of the first battery cell is denoted as .

[0108] The aforementioned battery cell observation function is a function used to describe the observation values ​​obtained from the battery state. For example, in Kalman filtering, the battery cell observation function is usually expressed as an observation equation.

[0109] In one possible implementation, the battery cell observation function characterizes the relationship between the battery cell voltage at the second target time and the battery cell open-circuit voltage, convective heat transfer coefficient, and second temperature difference of the first battery cell at the second target time; wherein, the second temperature difference is the temperature difference between the battery cell temperature at the second target time and the battery cell temperature of the first battery cell at the second target time, thereby predicting the battery cell voltage more quickly and accurately.

[0110] As one possible implementation method, the observation equation for a single battery cell is as follows:

[0111] in, This represents the cell voltage of the i-th battery cell. This indicates the open-circuit voltage of a single battery cell. This indicates that the i-th battery cell is at a temperature of... In this case, the prior estimate of the predicted value of the internal resistance of a single battery cell is... This represents the cell current of the i-th cell.

[0112] The formula for calculating the open-circuit voltage of the above-mentioned battery cell is as follows:

[0113] in, This represents the open-circuit voltage of a single battery cell at the second target time. This represents the open-circuit voltage of the first battery cell at the second target time. Indicates the convective heat transfer coefficient. This indicates the second temperature difference. This indicates the temperature of the first battery cell at the second target time. This indicates the temperature of the battery cell at the second target time.

[0114] In one possible implementation, the construction process of the above-mentioned correction function includes: constructing a first mapping relationship; wherein the first mapping relationship is used to characterize the correspondence between the observation residual of the battery cell at the second target time and the actual value and predicted value of the observation parameter of the battery cell at the second target time; constructing a second mapping relationship; wherein the second mapping relationship is used to characterize the correspondence between the gain coefficient corresponding to each state parameter of the battery cell at the second target time and the observation residual; constructing a third mapping relationship; wherein the third mapping relationship is used to characterize the correspondence between the state parameter correction value, gain coefficient and predicted value of the state parameter of each state parameter of the battery cell at the second target time; and generating a correction function based on the first mapping relationship, the second mapping relationship and the third mapping relationship, which can use the actual value of the observation parameter to correct the predicted value of the observation parameter in real time and accurately.

[0115] The observation residuals described above are used to describe the degree of inconsistency between the predicted and actual values ​​of the observed parameters. The closer the observation residuals are to zero, the higher the consistency between the predicted and actual values ​​of the observed parameters, and the more accurate the model prediction. Conversely, the lower the consistency between the predicted and actual values ​​of the observed parameters, and the worse the model prediction performance.

[0116] The aforementioned gain coefficients are used to weigh the contributions of predicted and actual observed parameter values ​​in state updates. The larger the gain coefficient, the higher the reliability of the actual observed parameter value, and the more the filter tends to correct the predicted observed parameter value with the actual observed parameter value, resulting in a greater degree of correction. Conversely, the smaller the gain coefficient, the higher the reliability of the predicted observed parameter value, and the more the filter tends to correct the actual observed parameter value, resulting in a smaller degree of correction. For example, the aforementioned gain coefficients can be the gain coefficients of the Kalman gain.

[0117] The Kalman gain mentioned above is used to dynamically adjust the weights of the predicted and measured values ​​in the state estimation, and is a parameter calculated by the Kalman filter when updating the state estimation.

[0118] The third mapping relationship described above is used to describe the relationship between the state parameter correction value, the state parameter correction increment, and the state parameter prediction value at the second target time.

[0119] The aforementioned state parameters are physical quantities used to describe the state of a single battery cell, reflecting its performance or health status.

[0120] The aforementioned state parameter correction value refers to the correction value obtained after correcting the predicted state parameter value based on the actual state parameter value at the second target time.

[0121] In one possible implementation, the third mapping relationship is used to characterize the correspondence between the state parameter correction value of each state parameter of the battery cell at the second target time, the state parameter correction increment, and the state parameter prediction value; wherein, the state parameter correction increment is determined based on the polarization voltage of the battery cell at the second target time and the gain coefficient of each state parameter of the battery cell at the second target time.

[0122] The aforementioned state parameter correction increment is an adjustment to the predicted values ​​of the state parameters, used to compensate for model errors or observation noise, etc.

[0123] The aforementioned polarization voltage refers to the deviation between the actual voltage and the open-circuit voltage during the charging and discharging process of the battery, which is caused by electrode reaction kinetics and mass transfer processes.

[0124] In one possible implementation, the second mapping relationship characterizes the correspondence between the gain coefficient of each state parameter of a battery cell at the second target time and the observation noise covariance, prior error covariance, and Jacobian matrix of each state parameter of the battery cell at the second target time. The prior error covariance is determined based on the Jacobian matrix and the process noise covariance and posterior error covariance of each state parameter of the battery cell at the second target time. The process noise covariance and observation noise covariance are determined based on the observation residuals. The Jacobian matrix characterizes the partial derivative of the predicted observation parameter value of the battery cell at the second target time with respect to the predicted state parameter value of each state parameter of the battery cell at the second target time. The posterior error covariance is determined based on the prior error covariance and the gain coefficient, ensuring that the state parameter value adjusted based on the gain coefficient is closer to the actual value of the state parameter, thereby improving the accuracy and robustness of the battery cell state prediction.

[0125] As one possible approach, the aforementioned gain coefficient can be a Kalman gain. In the case of a Kalman gain, the process of determining the gain coefficient includes a prediction phase, an update phase, an adaptive noise adjustment phase, and a charge state drift compensation phase.

[0126] Prediction phase: Based on the previous state and input, calculate the predicted state value: And update the prediction error covariance:

[0127] in, This represents the prediction error covariance matrix of the i-th battery cell at time k+1. Represents the Jacobian matrix. Let the prediction error covariance matrix at time k be denoted as . This represents the process noise covariance matrix, used to reflect environmental disturbances and model uncertainties. This represents the transpose of the Jacobian matrix.

[0128] Update phase: Based on the actual voltage value y i (k+1), Calculate the observation residuals: R i (k+1)=y i (k+1)-h(x i - (k+1),u i (k+1)) Where, x i - (k+1) represents the prior state estimate of the state parameter at time k+1, u i(k+1) represents the input value of the voltage at time k+1, i.e., the posterior state estimate of the voltage at time k, h(x) i - (k+1),u i (k+1) is a function used to characterize the state parameters and observation parameters at time k+1. And calculate the Jacobian matrix:

[0129] Kalman gain is calculated as follows: K i (k+1)=P i - (k+1) T [ T +R i ] -1 Updated estimates: x i (k+1)=x i - (k+1)+K i (k+1)R i (k+1) Where, x i (k+1) represents the posterior state estimate at time k+1, x i - (k+1) represents the prior state estimate at time k+1, K i (k+1) is the gain coefficient at time k+1, R i (k+1) represents the observation residual of the i-th cell at time k+1.

[0130] Update the state error covariance matrix: P i (k+1)=(1-K i (k+1)H i )P i - (k+1) Among them, P i (k+1) represents the posterior covariance matrix at time k+1, P i - (k+1) represents the prior covariance matrix at time k+1.

[0131] The adaptive noise adjustment stage is designed to enhance the stability and adaptability of the model under different operating conditions.

[0132] Noise covariance includes process noise covariance Q _iCovariance of observation noise R _i Updated in real time based on the predicted residual statistics: R i (k+1)=(1-λ R )R i (k)+λ R [R i (k+1)r i T (k+1)] Q i (k+1)=(1-λ Q )Q i (k)+λ Q [K i (k+1)r i (k+1)] Among them, Q i (k+1) represents the process noise covariance matrix at time k+1, R i (k+1) represents the observation noise covariance matrix at time k+1, λ R λ represents the adaptive step size coefficient of the observation noise covariance. Q K represents the adaptive step size coefficient of the process noise covariance. i (k+1) represents the Kalman gain term of the i-th cell at time k+1, R i (k) represents the observation noise covariance matrix of the i-th battery cell at time k, Q i (k) represents the process noise covariance matrix at time k, r i (k+1) represents the observation residual of the i-th cell at time k+1.

[0133] SOC Drift Compensation Stage: During long-term operation, SOC estimation errors accumulate due to capacity decay, temperature fluctuations, and current sampling errors. To suppress drift, the capacity Q is... i As an estimable state variable, it enables the filter to dynamically correct its state of charge (SOC) during operation.

[0134] Define SOC drift error:

[0135] in, Let be the predicted SOC value of the i-th battery cell at time k. Let SOC be the predicted value of the first battery cell at time k.

[0136] The correction equation based on the gain coefficient is then:

[0137] in, This represents the gain coefficient of the i-th battery cell. This represents the actual voltage value of the i-th battery cell at time k. This represents the predicted voltage value of the i-th battery cell at time k. This represents the Joule heat of the i-th cell at time k. This represents the cell current of the i-th cell at time k. This represents the time interval between time k and time k+1.

[0138] In one possible implementation, a battery state model is constructed based on the battery cell state transition function, battery cell observation function, correction function, and the observed and state parameter values ​​of the battery cells. This includes: constructing a verification model; wherein the verification model is used to verify whether the reconstructed cell voltage at the second target time satisfies the preset physical constraints; and constructing the battery state model using the battery cell state transition function, battery cell observation function, correction function, verification model, and the observed and state parameter values ​​of the battery cells. In this battery state model, replacing the actual observed parameter values ​​of the battery cells at the second target time with the reconstructed cell voltage that satisfies the preset physical constraints allows the battery state model to better reflect the actual operating conditions of the power battery.

[0139] The above verification model is used to verify whether the reconstructed cell voltage of a battery cell at the second target time meets the preset physical constraints.

[0140] The reconstructed cell voltage mentioned above is the voltage value obtained by correcting the predicted value of the observed parameters based on the actual value of the observed parameters of the cell at the second target time.

[0141] In one possible implementation, the battery cell state model includes: a voltage model, a thermal effect model, and a health state model. The voltage model characterizes the relationship between the battery cell voltage, battery cell electromotive force, and battery cell voltage loss; the voltage loss includes polarization loss and inductive voltage loss. The thermal effect model characterizes the relationship between the battery cell temperature, battery cell initial temperature, specific heat capacity, battery cell thermal power, and battery cell mass. The health state model characterizes the relationship between the battery cell state of charge, battery cell initial state of charge, battery cell rated capacitance, battery cell actual capacitance, and battery cell net current; the battery cell net current is the difference between the battery cell open-circuit current and the battery cell loss current, enabling more comprehensive prediction of the battery cell and more accurately reflecting its actual state.

[0142] As one feasible approach, the cell voltage in the above voltage model can be expressed by the following formula:

[0143] in, Indicates the voltage of a single battery cell. This represents the electromotive force of a single battery cell. This indicates the voltage loss of a single battery cell.

[0144] In the calculation of battery voltage loss, polarization loss and inductive voltage loss are taken into account. Polarization loss includes voltage loss based on battery internal resistance, voltage loss based on charge diffusion, and battery voltage loss based on the double layer. Inductive voltage loss includes battery voltage loss based on inductance.

[0145] The voltage loss based on the battery's internal resistance mentioned above refers to the voltage drop that occurs when the battery discharges, as current flows through the internal resistance, resulting in an output voltage lower than the battery's nominal voltage. The magnitude of the voltage loss is proportional to the product of the current and the internal resistance.

[0146] The internal resistance of a battery refers to the resistance encountered when current flows through the battery. It consists of two parts: ohmic internal resistance and polarization resistance. Ohmic internal resistance is determined by the resistive characteristics of the battery materials themselves, including the positive electrode material, negative electrode material, electrolyte, and separator. Polarization resistance is the resistance generated by electrochemical reactions during battery discharge, mainly originating from electrochemical polarization phenomena such as hydrogen evolution polarization and concentration polarization. This loss can be expressed as:

[0147] in, This represents the voltage drop based on the internal resistance of a single battery cell. Indicates the current of a single battery cell. Indicates the temperature of a single battery cell. Indicates the internal resistance of a single battery cell. It is a function of state of charge, battery current, and battery temperature.

[0148] The voltage loss based on charge diffusion mentioned above refers to the voltage loss caused by the diffusion behavior of individual battery cells.

[0149] The diffusion behavior of the aforementioned battery cells refers to the diffusion of ions, electrons, or other charge carriers through battery materials (such as the positive electrode, negative electrode, and electrolyte) within the battery, especially during charging and discharging. This diffusion behavior has a significant impact on the performance, efficiency, and lifespan of the battery cells. The diffusion behavior mainly involves ion diffusion and charge carrier migration.

[0150] Ion diffusion can be described as the migration of positive and negative ions. Within a single battery cell, the migration of positive and negative ions between the positive and negative electrodes is a core process in battery operation. For example, in a lithium-ion battery, lithium ions diffuse from the positive electrode to the negative electrode (during discharge) and from the negative electrode back to the positive electrode (during charging). The rate of ion diffusion directly affects the battery's charging and discharging speed and power output.

[0151] Electron migration is also part of diffusion. The movement of electrons mainly occurs in the electrode material and the current collector, and their migration rate determines the current transfer efficiency.

[0152] At high current densities, the ion diffusion rate may not meet the requirements, leading to a decline in battery performance, such as voltage drop and reduced energy efficiency. This loss can be expressed as:

[0153] in, This represents the voltage loss of a single battery cell based on charge diffusion. This indicates the diffusion capacitance value of a single battery cell. This indicates the diffusion resistance value of a single battery cell. This indicates the current of a single battery cell.

[0154] The aforementioned battery voltage loss based on the electric double layer refers to the voltage loss caused by the formation and changes of the electric double layer between the electrode surface and the electrolyte during battery charging and discharging. Specifically, at the interface between the electrode and the electrolyte, the redistribution of charge forms the electric double layer. This process consumes a certain amount of energy, resulting in a decrease in the battery terminal voltage. The electric double layer voltage loss is closely related to factors such as the charging rate, the surface properties of the electrode material, the properties of the electrolyte, and the operating temperature. This loss can be expressed as: ; in, This represents the voltage loss of the double layer based on a single battery cell. This represents the double-layer capacitance of a single battery cell. This represents the double-layer resistance value of a single battery cell. This indicates the current of a single battery cell.

[0155] The aforementioned battery voltage loss based on inductance refers to the inductive reactance that creates a magnetic field when current passes through the inductor, which impedes changes in current.

[0156] The aforementioned inductor is a component that stores magnetic energy, and its impedance (inductive reactance) increases proportionally to the circuit frequency. A back electromotive force (EMF) is generated across the inductor, its direction opposite to the current direction. This back EMF increases the total voltage drop of the circuit, thereby reducing the effective output voltage of the battery. The magnitude and duration of the back EMF affect the degree of battery voltage loss. This loss can be expressed as: ; in, This represents the voltage drop of a single battery cell based on its inductance. This indicates the inductance value of a single battery cell.

[0157] As a feasible approach, the voltage model is also used to calculate the internal resistance of a battery cell based on its state of charge (SOC) and temperature using a two-dimensional interpolation table.

[0158] As an feasible approach, the thermal effect model is a model that simulates the temperature of individual battery cells and the thermal interaction between them. The determination of the temperature of individual battery cells in the thermal effect model needs to consider the generation and loss of battery heat. The generation of battery heat includes the generation of heat from battery chemical reactions, the generation of heat from gas release, the generation of Joule heat from current, and the heat conduction between battery cells; the heat loss includes radiative heat loss and cooling heat loss.

[0159] For example, the formula for calculating the temperature of a single battery cell is as follows:

[0160] in Indicates the temperature of a single battery cell. This indicates the initial temperature of the battery cell. This indicates the specific heat capacity of a single battery cell. Indicates the mass of a single battery cell. This indicates the thermal power of a single battery cell.

[0161] The calculation of the thermal power of a single battery cell involves the heat generation and heat loss of various parts, which can be expressed by the following formula:

[0162] in, Indicates the heat generated by the battery chemical reaction, Indicates the generation of heat by the release of gas. This indicates that the Joule heating of the electric current is generated. This indicates heat conduction between individual battery cells. Indicates radiative heat loss. This indicates heat loss due to cooling.

[0163] The heat generation from the aforementioned battery chemical reaction refers to the heat released during the battery's chemical reaction process. This heat is primarily due to the thermal effect of the chemical reaction itself and the internal resistance loss of the battery. During battery discharge, the chemical reaction converts chemical energy into electrical and thermal energy. The energy changes resulting from the breaking and formation of chemical bonds cause some energy to be released as heat. Furthermore, the ohmic internal resistance and polarization resistance of the battery inevitably generate Joule heat during current flow. These factors work together to cause the battery to heat up during operation, leading to a temperature rise. This heat can be expressed as:

[0164] in, This indicates the heat generated by the battery's chemical reaction. It represents the heat coefficient of a chemical reaction.

[0165] The aforementioned gas release and heat generation refers to the phenomenon where gas is generated and released inside the battery during charging and discharging due to chemical reactions or other factors. This phenomenon is usually closely related to the battery's operating state and design, and is particularly pronounced under conditions of overcharging, high temperatures, or improper use. During charging, when the charging current exceeds the battery's capacity or the charging voltage is too high, the battery may enter an overcharged state. At this time, the water decomposition reaction is triggered, producing hydrogen and oxygen. During discharging, the battery may also produce trace amounts of gas, especially when approaching a fully discharged state; this phenomenon can cause current loss. This can be described by the following formula:

[0166] in This indicates the voltage of a single battery cell. This represents the current loss of a single battery cell, which can be expressed as:

[0167] in This indicates the current loss of a single battery cell under rated operating conditions. Indicates the rated voltage of a single battery cell. Indicates battery temperature. Indicates the rated temperature. Indicates the voltage coefficient. This represents the temperature coefficient.

[0168] The aforementioned Joule heating refers to the heat generated when current flows through various conductors and chemical reaction media within a battery during operation. Due to the inherent resistance within the battery, and according to Joule's law, the current passing through this resistance generates heat. This heat is called Joule heating. The magnitude of Joule heating is directly proportional to the square of the current, the resistance, and the time.

[0169]

[0170] in Indicates the current of a single battery cell. This indicates the voltage drop of a single battery cell.

[0171] The aforementioned inter-cell thermal conduction refers to the heat generated during battery operation, especially during charging and discharging. This heat is transferred to other areas outside or inside the battery through thermal conduction caused by the contact between the cells, thereby preventing localized overheating that could lead to performance degradation or damage. Conducted heat can be expressed as:

[0172] in, This indicates the thermal conductivity contact area of ​​a single battery cell. Indicates thermal conductivity, Indicates the distance between two individual battery cells. Indicates the current cell temperature of the battery cell. This indicates the temperature of any cell in the power battery other than the current cell.

[0173] The aforementioned radiative heat loss is a phenomenon where an object with a high temperature emits electromagnetic waves and releases heat into its surroundings, resulting in heat loss. In battery systems, this thermal radiation is mainly concentrated in the edge regions of the battery, which are typically in direct contact with the surrounding environment. Radiative heat exchange can be represented as:

[0174] in, Indicates the thermal diffusivity. This indicates the radiation area of ​​a single battery cell. Boltzmann's constant, This indicates the ambient temperature of the current battery cell.

[0175] Cooling heat loss refers to the heat flux density model added to the top, bottom, front, rear, left, and right sides of the battery pack based on actual battery thermal management conditions. For each heat-generating component, the heat loss generated by the corresponding cooling system can be expressed by the following formula:

[0176] in, Represents heat flux density, This indicates the contact area of ​​the cooling system.

[0177] As an feasible approach, the key parameter of the health state model is the state of charge of the individual battery cells.

[0178] The state of charge (SOC) of a battery cell indicates the percentage of its total capacity currently stored in that cell. It is a core indicator for measuring battery energy storage levels, typically expressed as a percentage, ranging from 0% (fully discharged) to 100% (fully charged). Accurate estimation and management of SOC are crucial for optimizing battery system performance, extending lifespan, and ensuring safe operation. SOC is influenced by various factors, including battery capacity, charge / discharge current, and temperature. In this application, the combined effects of current loss and charge / discharge current are used to calculate SOC, expressed by the following formula:

[0179] in, This represents the initial value of the state of charge (SOC) of a single battery cell. This indicates the rated capacitance of a single battery cell. Indicates the health status of individual battery cells. This indicates the open-circuit current of a single battery cell. This indicates the current loss of a single battery cell.

[0180] Figure 6 This is a block diagram of a modeling device for a power battery shown in an embodiment of this application, with reference to... Figure 6 The power battery modeling device includes: a single cell modeling module 601, a parameter determination module 602, and a model construction module 603.

[0181] The single-cell modeling module 601 is used to construct the single-cell state model of the first battery cell; wherein, the single-cell state model is used to determine the state parameter values ​​of the first battery cell based on the actual values ​​of the observed parameters of the first battery cell; the first battery cell is any battery cell among the battery cells included in the power battery.

[0182] The parameter determination module 602 is used to determine the state parameter value of the second battery cell by utilizing the difference between the observed actual values ​​of the first battery cell and the state parameter value of the second battery cell; the second battery cell is a battery cell other than the first battery cell.

[0183] The model building module 603 is used to construct a battery state model that couples the electrical, thermal and health states of a power battery using the observed parameter values ​​and state parameter values ​​of individual battery cells.

[0184] In one possible implementation, the state parameters referred to by the state parameter values ​​include: electrical state parameters, thermal state parameters, and health state parameters; a model building module is used to construct the battery cell state transition function; wherein, the battery cell state transition function is used to characterize the state parameter change relationship between the state parameter value of the battery cell at the first target time and the predicted state parameter value of the battery cell at the second target time, given the observed parameter value of the battery cell at the first target time; wherein, the second target time is the time following the first target time; the state parameter change relationship is a coupled change relationship of electrical parameters, thermal parameters, and health state parameters; a battery cell observation function is constructed; wherein, the battery cell observation function characterizes the correspondence between the predicted state parameter value and the observed parameter prediction value of the battery cell at the second target time; a correction function is constructed; wherein, given the actual observed parameter value of the battery cell at the second target time, the correction function is used to correct the predicted state parameter value of the battery cell at the second target time; based on the battery cell state transition function, the battery cell observation function, the correction function, and the observed parameter value and state parameter value of the battery cell, a battery state model is constructed.

[0185] In one possible implementation, the process of constructing the correction function includes: constructing a first mapping relationship; wherein the first mapping relationship is used to characterize the correspondence between the observation residual of a battery cell at the second target time and the actual value and predicted value of the observation parameter of the battery cell at the second target time; constructing a second mapping relationship; wherein the second mapping relationship is used to characterize the correspondence between the gain coefficient of each state parameter of the battery cell at the second target time and the observation residual; constructing a third mapping relationship; wherein the third mapping relationship is used to characterize the correspondence between the state parameter correction value, gain coefficient and predicted value of the state parameter of each state parameter of the battery cell at the second target time; and generating a correction function based on the first mapping relationship, the second mapping relationship and the third mapping relationship.

[0186] In one possible implementation, the electrical state parameters include the internal resistance of the battery cell; the thermal state parameters include the temperature of the battery cell; the health state parameters include the state of charge of the battery cell; and the observed parameters indicated by the actual values ​​include the voltage of the battery cell.

[0187] In one possible implementation, the battery cell state transition function includes: a battery cell state of charge transition function, a battery cell temperature transition function, and a battery cell internal resistance transition function; wherein, the battery cell state of charge transition function characterizes the state of charge of the battery cell at a second target time, and the correspondence between the battery cell state of charge at the first target time and the battery cell current; the battery cell temperature transition function characterizes the battery cell temperature at the second target time, and the correspondence between the battery cell temperature at the first target time, the battery cell current, the battery cell internal resistance, and a first temperature difference; the first temperature difference is the difference between the battery cell temperature and the ambient temperature; the battery cell internal resistance transition function characterizes the battery cell internal resistance at the second target time, and the correspondence between the battery cell internal resistance, the reference internal resistance, the thermal conductivity coefficient, and the second temperature difference; the second temperature difference is the temperature difference between the battery cell temperature at the second target time and the battery cell temperature at the second target time of the first battery cell.

[0188] In one possible implementation, the battery cell observation function is used to characterize the relationship between the battery cell voltage at the second target time and the battery cell open-circuit voltage, convective heat transfer coefficient, and second temperature difference of the first battery cell at the second target time; wherein, the second temperature difference is the temperature difference between the battery cell temperature at the second target time and the battery cell temperature of the first battery cell at the second target time.

[0189] In one possible implementation, the second mapping relationship is used to characterize the correspondence between the gain coefficient of each state parameter of a battery cell at the second target time and the observation noise covariance, prior error covariance, and Jacobian matrix of each state parameter of the battery cell at the second target time. The prior error covariance is determined based on the Jacobian matrix and the process noise covariance and posterior error covariance of each state parameter of the battery cell at the second target time. The process noise covariance and observation noise covariance are determined based on the observation residuals. The Jacobian matrix is ​​used to characterize the partial derivative of the predicted observation parameter value of the battery cell at the second target time with respect to the predicted state parameter value of each state parameter of the battery cell at the second target time. The posterior error covariance is determined based on the prior error covariance and the gain coefficient.

[0190] In one possible implementation, the third mapping relationship is used to characterize the correspondence between the state parameter correction value of each state parameter of the battery cell at the second target time, the state parameter correction increment, and the state parameter prediction value; wherein, the state parameter correction increment is determined based on the polarization voltage of the battery cell at the second target time and the gain coefficient of each state parameter of the battery cell at the second target time.

[0191] In one possible implementation, the model building module is also used to build a verification model; wherein the verification model is used to verify whether the reconstructed cell voltage of the battery cell at the second target time meets the preset physical constraints; the battery state model is constructed using the battery cell state transition function, battery cell observation function, correction function, verification model, and the observed parameter values ​​and state parameter values ​​of the battery cell; wherein the battery state model uses the reconstructed cell voltage that meets the preset physical constraints to replace the actual values ​​of the observed parameters of the battery cell at the second target time.

[0192] In one possible implementation, the battery cell state model includes: a voltage model, a thermal effect model, and a health state model; wherein, the voltage model is used to characterize the correspondence between the battery cell voltage and the battery cell electromotive force and the battery cell voltage loss; the voltage loss includes polarization loss and inductive reactance voltage loss; the thermal effect model is used to characterize the correspondence between the battery cell temperature and the battery cell initial temperature, specific heat capacity, battery cell thermal power, and battery cell mass; the health state model is used to characterize the correspondence between the battery cell state of charge and the battery cell initial state of charge, the battery cell rated capacitance, the battery cell actual capacitance, and the battery cell net current; the battery cell net current is the difference between the battery cell open-circuit current and the battery cell loss current.

[0193] Regarding the apparatus in the above embodiments, the specific methods of execution of each module have been described in detail in the embodiments of the power battery modeling method, and will not be elaborated here.

[0194] Figure 7 This is a block diagram illustrating an electronic device according to an embodiment of this application. Figure 7 As shown, the electronic device includes, but is not limited to, a processor 701 and a memory 702.

[0195] The memory 702 described above is used to store the executable instructions of the processor 701. It is understood that the processor 701 is configured to execute instructions to implement the power control method of the heat pump air conditioner in the above embodiment.

[0196] It should be noted that those skilled in the art will understand that Figure 7 The electronic device structure shown does not constitute a limitation on the electronic device; the electronic device may include, but is not limited to, other electronic devices. Figure 7 This may indicate more or fewer components, or combinations of certain components, or different component arrangements.

[0197] Processor 701 is the control center of the electronic device. It connects various parts of the electronic device via various interfaces and lines. By running or executing software programs and / or modules stored in memory 702, and by calling data stored in memory 702, it performs various functions and processes data, thereby providing overall monitoring of the electronic device. Processor 701 may include one or more processing units. Processor 701 may integrate an application processor and a modem processor. The application processor mainly handles the operating system, user interface, and applications, while the modem processor mainly handles wireless communication. It is understood that the modem processor may not be integrated into processor 701.

[0198] The memory 702 can be used to store software programs and various data. The memory 702 may primarily include a program storage area and a data storage area. The program storage area may store the operating system, application programs required by at least one functional module (such as deterministic components, integrated components, etc.), etc. Furthermore, the memory 702 may include high-speed random access memory, and may also include non-volatile memory, such as at least one disk storage device, flash memory device, or other volatile solid-state storage device.

[0199] In an exemplary embodiment, a computer-readable storage medium including instructions is also provided, such as a memory 702 including instructions, which can be executed by a processor 701 of an electronic device to implement the methods in the above embodiments.

[0200] In actual implementation, Figure 6 The functions of the single-unit modeling module 601, parameter determination module 602, and model construction module 603 can all be provided by... Figure 7 The processor 701 calls the computer program stored in the memory 702 to implement the process. The specific execution process can be found in the description of the method section in the previous embodiment, and will not be repeated here.

[0201] Optionally, the computer-readable storage medium may be a non-transitory computer-readable storage medium, such as a read-only memory (ROM), random access memory (RAM), compact disc read-only memory (CD-ROM), magnetic tape, floppy disk, and optical data storage device. In an exemplary embodiment, this application also provides a computer program product including one or more instructions, which can be executed by the processor 701 of an electronic device to perform the methods in the above embodiments.

[0202] It should be noted that when one or more instructions in the computer-readable storage medium or computer program product are executed by the processor of an electronic device, they implement the various processes of the above method embodiments and achieve the same technical effect as the above method. To avoid repetition, they will not be described again here.

[0203] Through the above description of the embodiments, those skilled in the art can clearly understand that, for the sake of convenience and brevity, only the division of the above functional modules is used as an example. In actual applications, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the device can be divided into different functional modules to complete all or part of the functions described above.

[0204] In the several embodiments provided in this application, it should be understood that the disclosed apparatus and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of modules or units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another apparatus, or some features may be ignored or not executed. Furthermore, the mutual coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between apparatuses or units may be electrical, mechanical, or other forms.

[0205] The units described as separate components may or may not be physically separate. A component shown as a unit can be one or more physical units; that is, it can be located in one place or distributed in multiple different locations. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0206] Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0207] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a readable storage medium. Based on this understanding, the technical solutions of the embodiments of this application, essentially, or the parts that contribute to the prior art, or all or part of the technical solutions, can be embodied in the form of a software product. This software product is stored in a storage medium and includes several instructions to cause a device (which may be a microcontroller, chip, etc.) or processor to execute all or part of the steps of the methods of the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, ROM, RAM, magnetic disks, or optical disks.

[0208] This application provides a computer program product containing instructions that, when run on a computer, cause the computer to perform the methods described in the above method embodiments.

[0209] This application also provides a computer-readable storage medium storing instructions that, when executed on a computer, cause the computer to perform the method in the method flow shown in the above method embodiments.

[0210] The computer-readable storage medium can be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of computer-readable storage media (a non-exhaustive list) include: an electrical connection having one or more wires, a portable computer disk, a hard disk, a random access memory, a read-only memory, an erasable programmable read-only memory, a register, a hard disk, an optical fiber, a portable compact disk read-only memory, an optical storage device, a magnetic storage device, or any suitable combination thereof, or any other form of computer-readable storage medium known in the art. An exemplary storage medium is coupled to a processor, enabling the processor to read information from and write information to the storage medium. Of course, the storage medium can also be a component of the processor. The processor and the storage medium can reside in an application-specific integrated circuit (ASIC). In embodiments of this application, the computer-readable storage medium can be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device.

[0211] Since the power battery modeling device, computer-readable storage medium, and computer program product in the embodiments of this application can be applied to the above method, the technical effects that can be obtained can also be referred to the above method embodiments. The embodiments of this application will not be repeated here.

[0212] The above are merely specific embodiments of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions within the technical scope disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A modeling method for a power battery, characterized in that, The modeling method for the power battery includes: A battery cell state model for a first battery cell is constructed; wherein, the battery cell state model is used to determine the state parameter values ​​of the first battery cell based on the actual values ​​of the observed parameters of the first battery cell; the first battery cell is any battery cell included in the power battery; The state parameter value of the second battery cell is determined by using the difference in actual observed parameter values ​​between the first and second battery cells and the state parameter value of the first battery cell; the second battery cell is the battery cell other than the first battery cell. Using the observed parameter values ​​and state parameter values ​​of the battery cells, a battery state model coupling the electrical, thermal, and health states of the power battery is constructed.

2. The modeling method for power batteries according to claim 1, characterized in that, The state parameters referred to by the state parameter values ​​include: electrical state parameters, thermal state parameters, and health state parameters; the construction of a battery state model coupling the electrical, thermal, and health states of the power battery using the observed parameter values ​​and state parameter values ​​of the battery cells includes: A state transition function for a battery cell is constructed. This function characterizes the relationship between the observed parameter values ​​of the battery cell at a first target time and the predicted state parameter values ​​at a second target time, given the observed parameter values ​​of the battery cell at a first target time. The second target time is the time following the first target time. The state parameter change relationship is a coupled relationship between electrical parameters, thermal parameters, and health state parameters. Construct a battery cell observation function; wherein, the battery cell observation function characterizes the correspondence between the predicted values ​​of the state parameters and the predicted values ​​of the observation parameters of the battery cell at the second target time; Construct a correction function; wherein, the correction function is used to correct the predicted value of the state parameter of the battery cell at the second target time, given the actual value of the observed parameter of the battery cell at the second target time; The battery state model is constructed based on the battery cell state transition function, the battery cell observation function, the correction function, and the observed parameter values ​​and state parameter values ​​of the battery cell.

3. The modeling method for power batteries according to claim 1, characterized in that, The process of constructing the correction function includes: Construct a first mapping relationship; wherein the first mapping relationship is used to characterize the correspondence between the observation residual of the battery cell at the second target time and the actual value and predicted value of the observation parameter of the battery cell at the second target time; A second mapping relationship is constructed; wherein, the second mapping relationship is used to characterize the correspondence between the gain coefficient of each state parameter of the battery cell at the second target time and the observation residual; A third mapping relationship is constructed; wherein, the third mapping relationship is used to characterize the correspondence between the state parameter correction value, gain coefficient and state parameter prediction value of each state parameter of the battery cell at the second target time; The correction function is generated based on the first mapping relationship, the second mapping relationship, and the third mapping relationship.

4. The modeling method for power batteries according to claim 2, characterized in that, The electrical state parameters include the internal resistance of the battery cell; the thermal state parameters include the temperature of the battery cell; the health state parameters include the state of charge of the battery cell. The observed parameters indicated by the actual values ​​of the observed parameters include the individual cell voltage.

5. The modeling method for power batteries according to claim 4, characterized in that, The battery cell state transition function includes: battery cell state of charge transition function, battery cell temperature transition function, and battery cell internal resistance transition function; The battery cell state of charge transition function is used to characterize the state of charge of the battery cell at the second target time and the correspondence between the state of charge of the battery cell at the first target time and the battery cell current. The battery cell temperature transfer function is used to characterize the battery cell temperature at the second target time and the correspondence between the battery cell temperature, battery cell current, battery cell internal resistance, and first temperature difference at the first target time; the first temperature difference is the difference between the battery cell temperature and the ambient temperature. The battery cell internal resistance transfer function is used to characterize the battery cell internal resistance at the second target time and the corresponding relationship between it and the reference internal resistance, thermal conductivity, and second temperature difference; the second temperature difference is the temperature difference between the battery cell temperature at the second target time and the battery cell temperature of the first battery cell at the second target time.

6. The modeling method for power batteries according to claim 4, characterized in that, The battery cell observation function characterizes the battery cell voltage at the second target time and the correspondence between the battery cell open-circuit voltage, convective heat transfer coefficient and second temperature difference of the first battery cell at the second target time. Wherein, the second temperature difference is the temperature difference between the battery cell temperature at the second target time and the battery cell temperature at the second target time.

7. The modeling method for power batteries according to claim 3, characterized in that, The second mapping relationship is used to characterize the correspondence between the gain coefficient of each state parameter of the battery cell at the second target time and the observation noise covariance, prior error covariance and Jacobian matrix of each state parameter of the battery cell at the second target time. The prior error covariance is determined based on the Jacobian matrix and the process noise covariance and posterior error covariance corresponding to each state parameter of the battery cell at the second target time. The process noise covariance and the observation noise covariance are determined based on the observation residuals; The Jacobian matrix is ​​used to characterize the predicted value of the observed parameters of the battery cell at the second target time, and the partial derivative of the predicted value of the state parameters of each state parameter of the battery cell at the second target time. The posterior error covariance is determined based on the prior error covariance and the gain coefficient.

8. The modeling method for a power battery according to any one of claims 3-7, characterized in that, The third mapping relationship is used to characterize the state parameter correction value of each state parameter of the battery cell at the second target time, the correspondence between the state parameter correction increment and the state parameter prediction value; The state parameter correction increment is determined based on the polarization voltage of the battery cell at the second target time and the gain coefficient of each state parameter of the battery cell at the second target time.

9. The modeling method for power batteries according to claim 4, characterized in that, The process of constructing the battery state model based on the battery cell state transition function, the battery cell observation function, the correction function, and the observed parameter values ​​and state parameter values ​​of the battery cell includes: A verification model is constructed; wherein the verification model is used to verify whether the reconstructed cell voltage of the battery cell at the second target time meets the preset physical constraint conditions; The battery state model is constructed using the battery cell state transition function, the battery cell observation function, the correction function, the verification model, and the observed and state parameter values ​​of the battery cell; wherein, in the battery state model, the reconstructed cell voltage that satisfies the preset physical constraints is used to replace the actual observed parameter values ​​of the battery cell at the second target time.

10. The modeling method for power batteries according to claim 1, characterized in that, The battery cell state model includes: voltage model, thermal effect model and health state model; The voltage model is used to characterize the relationship between the cell voltage, cell electromotive force, and cell voltage loss of the first battery cell; the voltage loss includes polarization loss and inductive voltage loss. The thermal effect model is used to characterize the relationship between the cell temperature of the first battery cell and the initial temperature, specific heat capacity, thermal power, and mass of the battery cell. The health state model is used to characterize the correspondence between the state of charge of the first battery cell and the initial state of charge, rated capacitance, actual capacitance and net current of the battery cell; the net current of the battery cell is the difference between the open circuit current and the loss current of the battery cell.

11. A modeling device for a power battery, characterized in that, The modeling device for the power battery includes: A single-cell modeling module is used to construct a single-cell state model of a first battery cell; wherein, the single-cell state model is used to determine the state parameter values ​​of the first battery cell based on the actual values ​​of the observed parameters of the first battery cell; the first battery cell is any one of the battery cells included in the power battery; The parameter determination module is used to determine the state parameter value of the second battery cell by utilizing the difference between the actual observed parameter values ​​of the first battery cell and the second battery cell and the state parameter value of the first battery cell; the second battery cell is a battery cell other than the first battery cell among the battery cells; The model building module is used to construct a battery state model that couples the electrical, thermal, and health states of the power battery using the observed parameter values ​​and state parameter values ​​of the battery cells.

12. A vehicle, characterized in that, The vehicle includes a power battery; the power battery is tested in-loop using a battery state model; the battery state model is constructed using the power battery modeling apparatus as described in claim 11.

Images